Besides, the CF-based tracking method mostly ignores the potential information about the multi-dimension response maps itself or from consecutive frames.
The responses generated by correlation filters indicate the importance of some particular pixels in the different feature dimensions and locations.
What is more, the temporal cues from video data can provide consistency constraints to improve tracking accuracy further.
Some algorithms [1]}, [2]} applied the color-based or iterated spatial masks to capture useful information related to tracked objects.
However, these methods can hardly cope with the occurrence of cluttered backgrounds and partial occlusion because they merely exploited few levels of information collaboratively and almost consider only a single frame.
